11 research outputs found
Visualizing Deep Networks by Optimizing with Integrated Gradients
Understanding and interpreting the decisions made by deep learning models is
valuable in many domains. In computer vision, computing heatmaps from a deep
network is a popular approach for visualizing and understanding deep networks.
However, heatmaps that do not correlate with the network may mislead human,
hence the performance of heatmaps in providing a faithful explanation to the
underlying deep network is crucial. In this paper, we propose I-GOS, which
optimizes for a heatmap so that the classification scores on the masked image
would maximally decrease. The main novelty of the approach is to compute
descent directions based on the integrated gradients instead of the normal
gradient, which avoids local optima and speeds up convergence. Compared with
previous approaches, our method can flexibly compute heatmaps at any resolution
for different user needs. Extensive experiments on several benchmark datasets
show that the heatmaps produced by our approach are more correlated with the
decision of the underlying deep network, in comparison with other
state-of-the-art approaches
Exploiting Record Similarity for Practical Vertical Federated Learning
As the privacy of machine learning has drawn increasing attention, federated
learning is introduced to enable collaborative learning without revealing raw
data. Notably, \textit{vertical federated learning} (VFL), where parties share
the same set of samples but only hold partial features, has a wide range of
real-world applications. However, existing studies in VFL rarely study the
``record linkage'' process. They either design algorithms assuming the data
from different parties have been linked or use simple linkage methods like
exact-linkage or top1-linkage. These approaches are unsuitable for many
applications, such as the GPS location and noisy titles requiring fuzzy
matching. In this paper, we design a novel similarity-based VFL framework,
FedSim, which is suitable for more real-world applications and achieves higher
performance on traditional VFL tasks. Moreover, we theoretically analyze the
privacy risk caused by sharing similarities. Our experiments on three synthetic
datasets and five real-world datasets with various similarity metrics show that
FedSim consistently outperforms other state-of-the-art baselines